Flaw Detection in Highly Scattering Materials Using a Simple Ultrasonic Sensor Employing Adaptive Template Matching

Ultrasonic sensors have been extensively used in the nondestructive testing of materials for flaw detection. For polycrystalline materials, however, due to the scattering nature of the material, which results in strong grain noise and attenuation of the ultrasonic signal, accurate detection of flaws is particularly difficult. In this paper, a novel flaw-detection method using a simple ultrasonic sensor is proposed by exploiting time-frequency features of an ultrasonic signal. Since grain scattering mostly happens in the Rayleigh scattering region, it is possible to separate grain-scattered noise from flaw echoes in the frequency domain employing their spectral difference. We start with the spectral modeling of grain noise and flaw echo, and how the two spectra evolve with time is established. Then, a time-adaptive spectrum model for flaw echo is proposed, which serves as a template for the flaw-detection procedure. Next, a specially designed similarity measure is proposed, based on which the similarity between the template spectrum and the spectrum of the signal at each time point is evaluated sequentially, producing a series of matching coefficients termed moving window spectrum similarity (MWSS). The time-delay information of flaws is directly indicated by the peaks of MWSSs. Finally, the performance of the proposed method is validated by both simulated and experimental signals, showing satisfactory accuracy and efficiency.

Model-based Intelligent Recognition for Aluminum Plate Seam Defects

In order to study the application of nonlinear ultrasonic in the quantitative identification of defective aluminum plate, different depth cracks are machined on the aluminum alloy plate with a thickness of 10 mm by wire cutting to simulate the defects in the plate. The normal and defective aluminum plates are selected to establish the experimental model, and the continuous wavelet transform (CWT) is used to extract the characteristic parameters of the aluminum plate nonlinear ultrasonic signal. The dimensions of the data are reduced by principal component analysis (PCA), and the principal component with the top three contribution rate are selected as the characteristic value. Finally, the support vector machine (SVM) algorithm is used to analyze the aluminum alloy plate state and classify the defect signal. The experimental results show that the feasibility of nonlinear ultrasonic signal recognition of aluminum plate defects is verified by combining principal component analysis and support vector machine model.

Fatigue Damage Detection and Risk Assessment via Wavelet Transform and Neural Network Analysis of Ultrasonic Signals

This paper develops a data-driven autonomous method for detection of fatigue damage and classification of the associated damage risk in mechanical structures, based on ultrasonic signal energy. The underlying concept is built upon attenuation of the signal and stability of the attenuation process. The attenuation provides pertinent information for damage quantification, whereas the stability represents resistance towards the fatigue damage growth. The proposed neural network (NN) model has been trained using the scaled conjugate-gradient back-propagation method. The NN model is capable of damage detection and damage classification into five classes of increasing risk. The Daubechies wavelet transform has been used to reduce the noisy pattern of the ultrasonic signal energy by using the associated approximation coefficients. The results show that the proposed method of approximation signal energy can detect and classify the damage with an accuracy of up to ∼ 9 8 . 5 % .

CREEPING SURFACE LONGITUDINAL ACOUSTIC WAVE: MAIN PROPERTIES AND APPLICATION POSSIBILITIES

Creeping ultrasonic waves have long been successfully used for flaw detection of near-surface and near-bottom zones of metal products. However, due to the fact that the creeping wave generates a lateral transverse wave directed into the metal volume at the third critical angle, it is also possible to test internal defects in principle. At known velocities of propagation of longitudinal and transverse waves in the metal, the third critical angle is easily calculated. Therefore, the time of propagation of the ultrasonic signal along any trajectory between points on the surface and in the volume of the metal can be calculated. Usually, creeping waves are used to test products of plane-parallel shape. There are no cases of their application on curved surfaces in the literature. It is possible that the creeping wave can also propagate over a concave surface. The aim of the article is to test experimentally new ways of using creeping waves. The propagation trajectories of the creeping and lateral transverse waves were studied on a steel plate. The time of passage of the ultrasonic signal along such trajectories of different lengths was measured, and the measurement results were compared with the calculated time values. The measured and calculated values coincided with accuracy sufficient for the coherent accumulation of echo signals that passed through the metal part of the path by the creeping wave and another part of the path by the lateral transverse wave.The propagation of the creeping wave over a concave surface was studied on a steel sample with cylindrical faces of different radii. As a result, it turned out that on a concave surface, the creeping wave propagates at the same speed of longitudinal waves as on a flat surface, but it decays much more strongly with distance. Studies have shown that creeping waves can be used in ultrasonic tomography, where a preliminary calculation of the propagation trajectories of ultrasonic signals is required. The propagation of creeping waves over concave surfaces extends the capabilities of the TOFD method to the area of intube testing